Skip to main content
SpringerLink
Log in

An Integrated MCI Detection Framework Based on Spectral-temporal Analysis

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

Aiming to differentiate between mild cognitive impairment (MCI) patients and elderly control subjects, this study proposes an integrated framework based on spectral-temporal analysis for the automatic analysis of resting-state electroencephalogram (EEG) recordings. This framework firstly eliminates noise by employing stationary wavelet transformation (SWT). Then, a set of features is extracted through spectral-temporal analysis. Next, a new wrapper algorithm, named three-dimensional (3-D) evaluation algorithm, is proposed to derive an optimal feature subset. Finally, the support vector machine (SVM) algorithm is adopted to identify MCI patients on the optimal feature subset. Decision tree and K-nearest neighbors (KNN) algorithms are also used to test the effectiveness of the selected feature subset. Twenty-two subjects are involved in experiments, of which eleven persons were in an MCI condition and the rest were elderly control subjects. Extensive experiments show that our method is able to classify MCI patients and elderly control subjects automatically and effectively, with the accuracy of 96.94% achieved by the SVM classifier. Decision tree and KNN algorithms also achieved superior results based on the optimal feature subset extracted by the proposed framework. This study is conducive to timely diagnosis and intervention for MCI patients, and therefore to delaying cognitive decline and dementia onset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. N. Houmani, F. Vialatte, E. Gallego-Jutglà, G. Dreyfus, V. H. Nguyen-Michel, J. Mariani, K. Kinugawa. Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework. PLoS One, vol. 13, no. 3, Article number e0193607, 2018. DOI: https://doi.org/10.1371/journal.pone.0193607.

    Article  Google Scholar 

  2. M. Kashefpoor, H. Rabbani, M. Barekatain. Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features. Journal of Medical Signals and Sensors, vol. 6, no. 1, pp. 25–32, 2016.

    Article  Google Scholar 

  3. S. Khatun, B. I. Morshed, G. M. Bidelman. Single channel EEG time-frequency features to detect mild cognitive impairment. In Proceedings of IEEE International Symposium on Medical Measurements and Applications, IEEE, Rochester, USA, pp. 437–442, 2017. DOI: https://doi.org/10.1109/MeMeA.2017.7985916.

    Google Scholar 

  4. V. C. Bibina, U. Chakraborty, R. M. Lourde, A. Kumar. Time-frequency methods for diagnosing Alzheimer’s disease using EEG: A technical review. In Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science, ACM, Singapore, pp. 49–54, 2017. DOI: https://doi.org/10.1145/3121138.3121183.

    Google Scholar 

  5. N. K. Al-Qazzaz, S. H. B. Ali, S. A. Ahmad, K. Chellappan, M. S. Islam, J. Escudero. Role of EEG as biomarker in the early detection and classification of dementia. The Scientific World Journal, vol. 2014, Article number 906038, 2014. DOI: https://doi.org/10.1155/2014/906038.

    Article  Google Scholar 

  6. Z. J. Yao, J. Bi, Y. X. Chen. Applying deep learning to individual and community health monitoring data: A survey. International Journal of Automation and Computing, vol. 15, no. 6, pp. 643–655, 2018. DOI: https://doi.org/10.1007/s11633-018-1136-9.

    Article  Google Scholar 

  7. F. Vecchio, C. Babiloni, R. Lizio, F. D. V. Fallani, K. Blinowska, G. Verrienti, G. Frisoni, P. M. Rossini. Resting state cortical EEG rhythms in Alzheimer’s disease: Toward EEG markers for clinical applications: A review. Supplements to Clinical Neurophysiology, vol. 62, pp. 223–236, 2013. DOI: https://doi.org/10.1016/B978-0-7020-5307-8.00015-6.

    Article  Google Scholar 

  8. V. Bajaj, S. Taran, A. Sengur. Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Information Science and Systems, vol. 6, no. 1, Article number 12, 2018. DOI: https://doi.org/10.1007/s13755-018-0048-y.

  9. S. Supriya, S. Siuly, H. Wang, Y. C. Zhang. An efficient framework for the analysis of big brain signals data. In Prceedings of Australasian Database Conference, Springer, Gold Coast, Australia, pp. 199–207, 2018. DOI: {rs 10.1007/978-3-319-92013-9_16 DOI}.

    Google Scholar 

  10. S. Supriya, S. Siuly, H. Wang, Y. C. Zhang. EEG sleep stages analysis and classification based on weighed complex network features. IEEE Transactions on Emerging Topics in Computational Intelligence, published online. DOI: https://doi.org/10.1109/TETCI.2018.2876529.

  11. P. A. M. Kanda, E. F. Oliveira, F. J. Fraga. EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer’s. Computer Methods and Programs in Biomedicine, vol. 138, pp. 13–22, 2017. DOI: https://doi.org/10.1016/j.cmpb.2016.09.023.

    Article  Google Scholar 

  12. H. Garn, C. Coronel, M. Waser, G. Caravias, G. Ransmayr. Differential diagnosis between patients with probable Alzheimer’s disease, Parkinson’s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalo-graphic features. Journal of Neural Transmission, vol. 124, no. 5, pp. 569–581, 2017. DOI: https://doi.org/10.1007/s00702-017-1699-6.

    Article  Google Scholar 

  13. S. Siuly, E. Kabir, H. Wang, Y. C. Zhang. Exploring sampling in the detection of multicategory EEG signals. Computational and Mathematical Methods in Medicine, vol. 2015, Article number 576437, 2015. DOI: https://doi.org/10.1155/2015/576437.

    Article  Google Scholar 

  14. M. Buscema, E. Grossi, M. Capriotti, C. Babiloni, P. Rossini. The I.F.A.S.T. model allows the prediction of conversion to Alzheimer disease in patients with mild cognitive impairment with high degree of accuracy. Current Alzheimer Research, vol. 7, no. 2, pp. 173–187, 2010. DOI: https://doi.org/10.2174/156720510790691137.

    Article  Google Scholar 

  15. E. Barzegaran, B. van Damme, R. Meuli, M. G. Knyazeva. Perception-related EEG is more sensitive to Alzheimer’s disease effects than resting EEG. Neurobiology of Aging, vol. 43, pp. 129–139, 2016. DOI: https://doi.org/10.1016/j.neurobiolaging.2016.03.032.

    Article  Google Scholar 

  16. P. Ghorbanian, D. M. Devilbiss, A. Verma, A. Bernstein, T. Hess, A. J. Simon, H. Ashrafiuon. Identification of resting and active state EEG features of Alzheimer’s disease using discrete wavelet transform. Annals of Biomedical Engineering, vol. 41, no. 6, pp. 1243–1257, 2013. DOI: https://doi.org/10.1007/s10439-013-0795-5.

    Article  Google Scholar 

  17. S. S. Poil, W. De Haan, W. M. van der Flier, H. D. Mansvelder, P. Scheltens, K. Linkenkaer-Hansen. Integrative EEG biomarkers predict progression to Alzheimer’s disease at the MCI stage. Frontiers in Aging Neuroscience, vol. 5, Article number 58, 2013. DOI: https://doi.org/10.3389/fnagi.2013.00058.

    Article  Google Scholar 

  18. F. Liu, X. S. Zhou, J. L. Cao, Z. Wang, H. Wang, Y. C. Zhang. Arrhythmias classification by integrating stacked bidirectional LSTM and two-dimensional CNN. In Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Macau, China, pp. 136–149, 2019. DOI: https://doi.org/10.1007/978-3-030-16145-3_11.

    Chapter  Google Scholar 

  19. L. R. Trambaiolli, N. Spolaôr, A. C. Lorena, R. Anghinah, J. R. Sato. Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease. Clinical Neurophysiology, vol. 128, no. 10, pp. 2058–2067, 2017. DOI: https://doi.org/10.1016/j.clinph.2017.06.251.

    Article  Google Scholar 

  20. R. F. Wang, J. Wang, S. N. Li, H. T. Yu, B. Deng, X. L. Wei. Multiple feature extraction and classification of electroencephalograph signal for Alzheimers’ with spectrum and bispectrum. Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 25, no. 1, Article number 013110, 2015. DOI: https://doi.org/10.1063/1.4906038.

    Article  MathSciNet  Google Scholar 

  21. A. I. Triggiani, V. Bevilacqua, A. Brunetti, R. Lizio, G. Tattoli, F. Cassano, A. Soricelli, R. Ferri, F. Nobili, L. Gesualdo, M. R. Barulli, R. Tortelli, V. Cardinali, A. Giannini, P. Spagnolo, S. Armenise, F. Stocchi, G. Buenza, G. Scianatico, G. Logroscino, G. Lacidogna, F. Orzi, C. Buttinelli, F. Giubilei, C. Del Percio, G. B. Frisoni, C. Babiloni. Classification of healthy subjects and Alzheimer’s disease patients with dementia from cortical sources of resting state EEG rhythms: A study using artificial neural networks. Frontiers in Neuroscience, vol. 10, Article number 604, 2017. DOI: https://doi.org/10.3389/fnins.2016.00604.

    Article  Google Scholar 

  22. F. Bertè, G. Lamponi, R. S. Calabrò, P. Bramanti. Elman neural network for the early identification of cognitive impairment in Alzheimer’s disease. Functional Neurology, vol. 29, no. 1, pp. 57–65, 2014.

    Google Scholar 

  23. S. Afrakhteh, M. R. Mosavi, M. Khishe, A. Ayatollahi. Accurate classification of EEG signals using neural networks trained by hybrid population-physic-based algorithm. International Journal of Automation and Computing, published online. DOI: https://doi.org/10.1007/s11633-018-1158-3.

    Article  Google Scholar 

  24. H. Aghajani, E. Zahedi, M. Jalili, A. Keikhosravi, B. V. Vahdat. Diagnosis of early Alzheimer’s disease based on EEG source localization and a standardized realistic head model. IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 6, pp. 1039–1045, 2013. DOI: https://doi.org/10.1109/JBHI.2013.2253326.

    Article  Google Scholar 

  25. I. Güler, E. D. Übeyli. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods, vol. 148, no. 2, pp. 113–121, 2005. DOI: https://doi.org/10.1016/j.jneumeth.2005.04.013.

    Article  Google Scholar 

  26. EEG Signals from Normal and MCI (Mild Cognitive Impairment) Cases, [Online], Available: http://www.biosigdata.com/?download=eeg-signals-from-normal-and-mci-cases, September 15, 2018.

  27. J. Vigil, L. Tataryn. Neurotherapies and Alzheimer’s: A protocol-oriented review. NeuroRegulation, vol. 4, no. 2, pp. 79–94, 2017. DOI: https://doi.org/10.15540/nr.4.2.79.

    Article  Google Scholar 

  28. B. T. Zhang, X. P. Wang, Y. Shen, T. Lei. Dual-modal physiological feature fusion-based sleep recognition using CFS and RF algorithm. International Journal of Automation and Computing, vol. 16, no. 3, pp. 286–296, 2019. DOI: https://doi.org/10.1007/s11633-019-1171-1.

    Article  Google Scholar 

  29. S. M. Hosni, M. E. Gadallah, S. F. Bahgat, M. S. Abdel-Wahab. Classification of EEG signals using different feature extraction techniques for mental-task BCI. In Proceedings of International Conference on Computer Engineering & Systems, IEEE, Cairo, Egypt, pp. 220–226, 2007. DOI: https://doi.org/10.1109/ICCES.2007.4447052.

    Google Scholar 

  30. F. Liu, X. S. Zhou, Z. Wang, J. L. Cao, H. Wang, Y. C. Zhang. Unobtrusive mattress-based identification of hypertension by integrating classification and association rule mining. Sensors, vol. 19, no. 7, Article number 1489, 2019. DOI: https://doi.org/10.3390/s19071489.

    Article  Google Scholar 

  31. D. Pandey, X. X. Yin, H. Wang, Y. C. Zhang. Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Computer Vision and Image Understanding, vol. 155, pp. 162–172, 2017. DOI: https://doi.org/10.1016/j.cviu.2016.12.005.

    Article  Google Scholar 

  32. N. K. Al-Qazzaz, S. H. B. M. Ali, S. A. Ahmad, M. S. Islam, J. Escudero. Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors, vol. 15, no. 11, pp. 29015–29035, 2015. DOI: https://doi.org/10.3390/s151129015.

    Article  Google Scholar 

  33. S. Siuly, V. Bajaj, A. Sengur, Y. C. Zhang. An advanced analysis system for identifying alcoholic brain state through EEG signals. International Journal of Automation and Computing, published online. DOI: https://doi.org/10.1007/s11633-019-1178-7.

    Article  Google Scholar 

  34. C. Lehmann, T. Koenig, V. Jelic, L. Prichep, R. E. John, L. O. Wahlund, Y. Dodge, T. Dierks. Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). Journal of Neuroscience Methods, vol. 161, no. 2, pp. 342–350, 2007. DOI: https://doi.org/10.1016/j.jneumeth.2006.10.023.

    Article  Google Scholar 

  35. J. C. McBride, X. P. Zhao, N. B. Munro, C. D. Smith, G. A. Jicha, L. Hively, L. S. Broster, F. A. Schmitt, R. J. Kryscio, Y. Jiang. Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease. Computer Methods and Programs in Biomedicine, vol. 114, no. 2, pp. 153–163, 2014. DOI: https://doi.org/10.1016/j.cmpb.2014.01.019.

    Article  Google Scholar 

  36. P. M. Rossini, M. Buscema, M. Capriotti, E. Grossi, G. Rodriguez, C. Del Percio, C. Babiloni. Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? Clinical Neurophysiology, vol. 119, no. 7, pp. 1534–1545, 2008. DOI: https://doi.org/10.1016/j.clinph.2008.03.026.

    Article  Google Scholar 

  37. G. Fiscon, E. Weitschek, A. Cialini, G. Felici, P. Bertolazzi, S. De Salvo, A. Bramanti, P. Bramanti, M. C. De Cola. Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Medical Informatics and Decision Making, vol. 18, Acticle number 35, 2018. DOI: https://doi.org/10.1186/s12911-018-0613-y.

    Article  Google Scholar 

Download references

Acknowledgements

The first author was supported by a La Trobe University Postgraduate Research Scholarship and a La Trobe University Full Fee Research Scholarship, she was also supported by the Science Foundation of Chongqing University of Arts and Sciences Chongqing, China (No. Z2016RJ15).

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, La Trobe University, Melboume, Victoria, 3083, Australia

    Jiao Yin & Jinli Cao

  2. School of Software Engineering, Chongqing University of Arts and Sciences, Chongqing, 402160, China

    Jiao Yin

  3. Institute for Sustainable Industries & Liveable Cities, Victoria University, Melboume, Victoria, 3083, Australia

    Siuly Siuly & Hua Wang

Authors
  1. Jiao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinli Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siuly Siuly
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiao Yin.

Additional information

Recommended by Associate Editor Victor Becerra

Jiao Yin received the B. Sc. degree in automation from Northeastern University, China in 2010, and the M. Sc. degree in vehicle operation engineering from Sun Yat-sen University, China in 2013. She is a Ph. D. degree candidate in computer science and computer engineering at Department of Computer Science and Information Technology, La Trobe University, Australia.

Her research interests include machine learning algorithms, data-based smart health monitoring and disease detection.

Jinli Cao received the Ph. D. degree from the University of Southern Queensland, Australia. She is a senior lecturer at Department of Computer Science and Information Technology, La Trobe University, Australia. She has published over 100 research papers in international conferences and journals such as IEEE Transactions on Distributed and Parallel Processing, IEEE Transactions on Knowledge and Data Engineering, Journal of Future Generation Computer Systems, and the top conferences such as International World Wide Web Conference (WWW), International Conference on Web Information Systems Engineering (WISE), International Conference on Advanced Information Systems Engineering (CAiSE) and International Conference on Database Systems for Advanced Applications (DASFAA), etc. She has 9 graduated Ph. D. students and is supervising 2 Ph. D. students currently.

Her research interests include the evolutionary fields of computer science, data engineering and information systems such as data quality, big data analytics, cloud computing, recommendation systems, query mining, data security, privacy protection, reliable queries in uncertain databases, top-k query ranking and decision supporting systems.

Siuly Siuly received the Ph. D. degree in biomedical engineering from the University of Southern Queensland, Australia in 2012. Currently, she is a research fellow with the Centre for Applied Informatics, College of Engineering and Science, Victoria University, Australia. She already developed some breakthrough methods in the mentioned areas. She made significant contributions to the mentioned research areas since the beginning of her Ph. D. in July 2008. Most recently, she has authored a book, titled: EEG Signal Analysis and Classification: Techniques and Applications published by Springer in December 2016. Currently, she is serving an associate editor of a prestigious journal IEEE Transactions on Neural Systems of Rehabilitation Engineering and also as the managing editor of Health Information Science and Systems.

Her research interests include biomedical signal processing, analysis and classification, detection and prediction of neurological abnormality from brain signal data (e.g., EEG data), brain-computer interface, machine learning, pattern recognition, artificial intelligence and medical data mining.

Hua Wang received the Ph. D. degree from the University of Southern Queensland, Australia. He is now a full-time professor at Victoria University, Australia. He was a professor at the University of Southern Queensland before he joined Victoria University, Australia. He has more than ten years teaching and working experience in applied informatics at both enterprise and university. He has expertise in electronic commerce, business process modelling and enterprise architecture. As a chief investigator, three Australian Research Council (ARC) Discovery grants have been awarded since 2006, and 200 peer-reviewed scholar papers have been published. Six Ph. D. students have already graduated under his principal supervision.

His research interests include data security, data mining, access control, privacy and web services, as well as their applications in the fields of e-health and e-environment.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yin, J., Cao, J., Siuly, S. et al. An Integrated MCI Detection Framework Based on Spectral-temporal Analysis. Int. J. Autom. Comput. 16, 786–799 (2019). https://doi.org/10.1007/s11633-019-1197-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-019-1197-4

Keywords

Search

Navigation